factor that influence teachers’ perception · pdf file ·...

International Journal of Scientific Research and Innovative Technology ISSN: 2313-3759 Vol. 4 No. 1; January 2017

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FACTOR THAT INFLUENCE TEACHERS’ PERCEPTION TOWARDS

THE IMPLEMENTATION OF STRENGTHENING MATHEMATICS AND

SCIENCE IN SECONDARY EDUCATION (SMASSE PROGRAMME) IN

BUNGOMA COUNTY, KENYA

PETER WAMALWA

MED/CIT/004/13

[email protected]

Dr. Edwin N. Masibo

Department of Curriculum and Instructional Technology

Kibabii University

Prof. Stanley N. Mutsotso

Department of Curriculum and Instructional Technology

Kibabii University


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ABSTRACT

Dismal performance in Mathematics and Science subjects poses a challenge towards realization of the Kenya

National development goal of industrialization by the year 2020. The government of Kenya in liaison with the

Japanese government came up with Strengthening of Mathematics and Science in Secondary Education

(SMASSE) to remedy the situation. This was to be achieved through in-service training for mathematics and

science teachers to improve on teacher classroom practices and student’s learning.This paper presents a

study on motivation as a strategy that influence mathematics and science teachers’ participation in the in-

service training. The study was guided by Rogers’s innovation-implementation diffusion theory. Descriptive

survey design was used and thetarget population was 1450 teachers teaching in 275 secondary schools and 9

sub-county Quality Assurance Officers (QASO). Simple random sampling and purposive sampling techniques

were used for a sample size of 438. Data was collected using questionnaires, interview schedules and an

observation guide. It was analyzed using both descriptive and inferential statistics and it was found out that

provision of motivation to teachers influence their participation. It was concluded that all teachers would

attend INSETs if motivated. From the conclusion, it was recommended that the national SMASSE office and

the MoEST should consider teachers’ views on motivation.

INTRODUCTION

Background

Modern learning theories emphasize that learners learn better if they are accorded autonomy in classroom,

time and facilities to construct knowledge for themselves and others (Brown, 1998). One of the goals of

education in Kenya is to prepare learners to contribute to the economic development of the country. In order to

realize this goal, it is envisaged that Mathematics and science will play a significant role. Because of this, the

Government has put emphasis on mathematics and science as being critical for the achievement of this goal.

According to Brown and Adams (2001), for such goal to be attained teachers must shift their attention away

from themselves as effective presenters of scientific informationto focus on student’s developmental needs to

learn science with understanding.

Efficient human capital development depends on the quality and effectiveness of teachers. It is probably for

this reason that the Koech report (RoK, 2000) recommended the Totally Integrated Quality Education and

Training (TIQET) for teacher trainees in teacher training institutionsapproved by thegovernment of Kenya.

The report of the National Committee on Educational objectives and policies (RoK, 1976) suggests that

improvement of the quality of the teacher is possible through training and retraining. It has been noted that

many teachers of science graduating from training institutions have not been exposed to all aspects of science

education (Hodson, 1993). He observed that teachers are ill prepared to teach effectively in the science

laboratory because they were brought up on a diet of content dominated cookery book type of practical work.

The Center for Mathematics, Science and Technology Education in Africa (CEMASTEA) aims at building

teachers’ capacities to enablethemcope with pedagogical related challenges encountered during curriculum

delivery. CEMASTEA coordinates SMASSE through In-Service Training (INSET) programmes. Skills


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acquired during SMASSE INSETs support the Social Pillar of Kenya’s Vision 2030 (RoK, 2012). The

SMASSE Project Impact Assessment Survey (SPIAS) results indicate that the level of implementation of

Activity-focused, Student-centered, Experimenting and Improvisation through Plan, Do, See and Improve

(ASEI-PDSI) classroom practices innovation is low (SMASSE, 2002). This implies a glaring industrial skills

gap in Kenya. The alarming poor performance in mathematics and sciences puts the prospects of Kenya

becoming industrialized nation in jeopardy.

The SMASSE project was therefore,initiated to address the following factors deemed to affect the

performance of Mathematics and Sciences: teachers and learners attitude; teaching methodology; teachers’

mastery of content and development of teaching and learning materials (SMASSE, 1998). These factors were

to be addressed through INSETs for Mathematics and Science teachers in the whole country. Despite the

efforts and the objectives of the SMASSE project aimed at improving performance of mathematics and

science, very little has changed over the years. The continuous poor performance by students in these subjects

in national examinations has drawn concern from various stakeholders.

Mathematics and science teachers are expected to attend all the four INSET cycles as planned by the MoEST.

In cycle one; INSET emphasis is laid on attaining a positive attitude towards these subjects among the

stakeholders, the teachers and the learners.In cycle two, INSETs adopt a more practical oriented approach by

providing hands-on experience. This cycle provides opportunities to put into practice the principles of the

ASEI movement and PDSI approach. Cycle three focuses on classroom implementation of the ASEI/PDSI

classroom practices and, cycle four involves monitoring and evaluation which aims at improving the quality of

the project activities.

In Bungoma County, the training takes place at Cardinal Otunga Girl’s High school, Bungoma High School,

Friends School Kamusinga and Lugulu Girls’s High school. Despite the government’s involvement as a matter

of policy, the number of teacher’s attending SMASSE in-service training has continued to decrease as shown

in table 1.2 below.

Table 1.2 Mathematics and science teachers attending SMASSE INSET between 2010 and 2013

County Director of Education (2014)

There have also been cases of discontent amongst the participants during in-service training (QASO, 2014),

and performance in these key subjects continue to be dismal causing concern to all education stakeholders as

to what ails education in Bungoma County. This is the scenario necessitated an investigation on how

INSET Center. 2010 2011 2012 2013

Cardinal 295 250 245 188

Kamusinga 420 367 340 242

Lugulu 395 333 314 198

Bungoma 340 318 255 216


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motivation factor influence teachers’ perception on the implementation of SMASSE programme in Bungoma

County.

OBJECTIVE

.The objective of this paper was to find out the influence of motivational strategies on the teachers’

participation in the SMASSE in-service training Programme in Bungoma County.

RESEARCH METHODOLOGY

Introduction

This part of paper presents the research design; location of the study; target population; sample size and

sampling techniques; data collection instruments; data collection procedure; validity and reliability of the

research instruments; data collection and analysis procedures.

Research Design

This study was conducted through descriptive survey design. Descriptive survey involves collecting the

information by interviewing or administering a questionnaire to a sample of individuals (Orodho, 2003). This

design enabled the researcher to determine the present status of the population of the study with regard to a

number of variables. It enabled the researcher to collect information about teachers’ perception towards

implementation of SMASSE ASEI-PDSI tenets in classroom teaching and learning. The study examined the

situation as it is in Bungoma County. According to Mugenda (2003), descriptive survey involves collection of

data in order to determine whether and to what degree a relationship exists between two or more quantifiable

variables.

Area of Study

This study was conducted in the secondary schools in Bungoma County together with sub-county Quality

Assurance and Standards Officer (QASO). Bungoma is one of the four counties in the former Western

province in Kenya. It is bordered by Busia County to the South, Kakamega County to the West and Trans

Nzoia County to the East. Figure 1 below shows the map of bungoma County.


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According to the Ministry of Planning and Development, the population of Bungoma County is estimated at

1,630,939 which make it the third most populated County in Kenya. The Taskforce Report on Education in

Bungoma County released in September, 2014 shows Performance in Mathematics and Science subjects have

been poor.The County was ranked last among the Counties bordering it.


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Study Population

The target population for this study was one thousand four hundred and fifty nine (1459). Out of this

population, one thousand four hundred and fifty (1450) weremathematics and science teachers, nine (9) Sub-

County QASOs. The researcher generalized the findings to this population. Table 3.1 shows target population

for schools per sub-county, number of mathematics and science teachers in each sub-county, number of

QASO and the INSET centres.

Table 3.1 Target Population

Sub-County No. of Schools No of Science &

Mathematics

Teacher

No of QASOs No of IN-SET

Centres

Bungoma Central 26 157 1 Nil

Bungoma East 48 218 1 1

Bungoma North 39 180 1 Nil

Bungoma South 43 250 1 2

Bungoma West 22 150 1 Nil

Bumula 36 198 1 Nil

Cheptais 14 45 1 Nil

Kimilili 31 165 1 1

Mt Elgon 16 87 1 Nil

Total 275 1450 9 4

Source: QASOs 2014 .

Sampling Techniques and Sample Size

The sample size was determined using the following technique;

Sampling Techniques

Since the study could not be conducted in all schools in Bungoma County, a representative sample was

selected from nine sub-counties for the study. Simple random sampling methodwas used to select 30%

secondary schools from each sub-county at a time that gave a representative sample. This was done using a

rotary method so that the remaining schools had equal opportunities of being picked. The researcher picked a

paper at a time and recorded the school’s namebefore picking the next school. The picking continued until

(30%) of the schools had been picked before sampling from the next sub-county. The process continued for all

the nine sub-counties and a total of 83 (30%) schools were sampled for this study as shown in table 3.2.


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Table 3.2 Sampling Procedure

Sub-County No. of schools per

sub-county, N

No. of schools

sampled, n, was

given by {n=0.3xN}

No. of teachers

sampledfor study

was given

by[Ts=0.3xteachers

per sub-county]

Bungoma Central 26 08 48

Bungoma East 48 14 66

Bungoma North 39 12 54

Bungoma South 43 13 76

Bungoma West 22 07 45

Bumula 36 11 60

Cheptais 14 04 14

Kimilili 31 09 50

Mt Elgon 16 05 22

Total 275 83 435

The researcher used systematic random sampling procedure to sample out mathematics and science teachers

per sub-county. All the possible respondents were index from 1 to a maximum in each sub-county and the

researcher picked respondents at an interval that provided 30% respondents from each sub-county. The

procedure was done for all the nine sub-counties and a total of 435 (30%) respondents were picked for this

study. The researcher used simple random sampling to select 3 (33%) QASOs.

Sample Size

The main factor to consider in determining the sample size is the need to keep it manageable enough (Orodho

and Kombo, 2002). Therefore by studying the sample, one can be able to know more about the population

without having to study the entire population. Although Bungoma County has 275 public secondary schools,

only 83 (30.2%) schools were randomly sampled. A total sample of 438 (30.0%) respondents was selected

from the target population for the study. This sample consisted of 435 (30%) teachers of mathematics and

sciences, and 3 (33.3%) sub-county QASOs. This is considered as a representative sample since it falls within

the range advocated by Mugenda and Mugenda (2003) who argues that, a representative sample for a

descriptive survey study that fulfils requirements of efficiency, reliabilityand flexibility, shouldbe in the range

of 20% to 30%. Table 3.2 shows respondents sample size.


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Table 3.3 Sample Size

Respondents Population

N

Sample

Population

n

Percentage

%

Sampling

Technique

Teachers 1450 435 30.0% Simple

Random

Sampling

QASOs 9 3 33.3% Purposive

Sampling

Sample Size 438 30.0%

Instruments for Data Collection

The research instruments for this study were Questionnaire, Observation and Interview schedules which were

developed by the researcher.

Questionnaire

The questionnaire is a research instrument that gathers data over a large sample (Kombo and Tromp, 2013). A

questionnaire ensures anonymity that gives respondents freedom to respond without fear of victimization

while allowing them to make their suggestions. The researcher used a questionnaire which had both open

ended and closed ended items. It had three sections. Section A sought the teachers’ background information

including age and academic/professional qualification. Section B had items that sought to establish how

motivational strategies influence respondents’ participation in the in-service training. Section E aimed at

finding suggestions for improvement of the implementation of SMASSE programme.

Interview Schedule

According to Koul (1993), interview method is often superior compared to other research tools. Once a

rapport has been established and confidence assured, certain confidential information can be divulged that

would otherwise have escaped the researcher (Platton, 1990). In addition, follow up can be made on incorrect

or incomplete answers to certain questions and the interviewer has the opportunity to gauge the sincerity of the

respondents’ information (Koul, 1993; Platton, 1990). This gave the researcher a complete and detailed

understanding of motivation provided to respondents and attributes of trainers.

International Journal of Scientific Research

Observation Schedule

It was used to obtain data on the use of A

able to evaluate the lesson sampled by in

techniques and improvisation during teac

Ethical Consideration

The permission to carry out this research

and the concerned parties. Permission wa

schools. The researcher kept any persona

to access it.

Data Analysis Procedure

Data was gathered from the field, coded

for Social Sciences (SPSS). Data collecte

FINDINGS

Gender of Respondents

There were 266 (62%) male and 163 (38

in-service training in Bungoma County.

Figu

ch and Innovative Technology ISSN: 2313-3759 Vo

37

f ASEI-PDSI practice during teaching and learnin

indicating the frequency use of teaching and lear

eaching and learning process.

ch and to use information obtained was sought fro

was obtained from the university, NACOSTI and

nal information confidential and did not allow any

ed and entered into the computer for analysis us

cted was presented using pie charts, tables and bar

(38%) female mathematics and science teachers w

y.

Male

62%

Female

38%

igure on Gender of the Respondents

ol. 4 No. 1; January 2017

ning. The researcher was

earning method, teaching

from relevant authorities

d the participating

ny unauthorized person

using Statistical Package

bar graphs.

who attended SMASSE


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Professional Qualification of the Respondents

Respondents were asked to indicate their professional qualifications and the findings were represented as

shown below.

The finding shows that 318 (74%) respondents were Bachelor of Education degree holders, 47 (11%)

respondents were holders Bachelor of Science with a Post Graduate Diploma in Education, 43 (10%) were

Diploma holders in Education while 21(5%) respondents were Master of Education Degree. The findings

indicate that all the mathematics and science teachers were professionally trained.

0

10

20

30

40

50

60

70

80

90

100

Diploma BED BA/BSC WITH

PGDE

MED

Pe

rce

nta

ge

Figure on Respondents' Professional Qualification

International Journal of Scientific Research

Subjects Taught by Respondent

The findings shows that 90 (21%)

Mathematics/Physics, 64 (15%) teach

established that 176 41%) respondents te

biology.

15%

6%

8%10% 13

mathematics and physics mathema

Biology and Chemistry Mathema

Mathematics and Business studies Biology

ch and Innovative Technology ISSN: 2313-3759 Vo

39

) respondents teach Mathematics and Chemis

h Chemistry/Biology and, 26 (6%) teach math

teach mathematics and other subject apart from

17%

21%

10%

13%

matics and chemistry physics and chemistry

matics and Biology Mathematics and Geography

y and Agriculture

ol. 4 No. 1; January 2017

istry, 73 (17%) teach

hematics/biology. It is

physics, chemistry and


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INSET Subject for Respondent

The finding shows that respondent participated in the in-service training as represented in the table below.

INSET Subjects for Respondents

SUBJECT FREQUENCY PERCENT

Biology 110 25.5

Chemistry 105 24.4

Mathematics 133 30.8

Physics 81 18.8

Total 429 100.0

From the data, 110 (26%) of the respondents attended Biology INSET, 103 (24%) Chemistry INSET and 81

(19%) respondents attended Physics INSET. A total of 133 (31%) respondents attended mathematics INSET.

This reveals that most mathematics teachers have attended SMASSE in-service training in Bungoma County.

Number of INSET Cycles Attended

Teachers are expected to attend all the four cycles of the SMASSE INSETs. Ifthey fail to attend any of them

they are given an opportunity to attend themop-up INSETs. The science and mathematics teachers are

expected to attend one INSET inone of their teaching subject areas.

Number of INSET Cycle Attended

Cycle Frequency percent

One 429 100

Two 400 93

Three 204 48

Four 155 36

From the table, 429 (100%) respondents attended cycle one of SMASSE in-service training, 400 (93.2%)

proceeded to attend cycle two. Only 204 (47.6%) respondents attended cycle three and 58 (13.5%)

respondents attended the fourth cycle.


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The main theme of cycle one is attitude change and pedagogical skills. Since 100% respondents attended in-

service training, it implies that all mathematics and science teachers benefited from the theme of cycle one and

therefore are able to influence learners’ attitude.

Participation in cycle two by respondents was 93%. Cycle two of SMASSE INSET provides an opportunity

for participants to put into practice the principles of ASEI-PDSI paradigm. The respondents work in groups in

order to focus on the problem areas of each subject. They also prepare ASEI lesson plans and present them to

their peers. Therefore, by simple calculation, it implies that 30 (7%) who did not attend the INSET missed the

“hands-on” training of the ASEI-PDSI classroom practices.

Cycle three of the INSET focuses on classroom implementation of the ASEI/PDSI principles. Activities of the

INSET have been designed to transform the concept of ASEI from the theory to practice, a process known in

SMASSE as actualization of ASEI. The cycle involves implementing of ASEI lessons and peer teaching

session done during cycle two into actual classroom implementation in schools within the locality. Lessons are

taught to different classes during the holidays (Waititu & Orado 2009). 225 (52%) respondents missed out on

the actualization of the ASEI-PDSI classroom practices. Cycle four of the INSET involves monitoring and

evaluation. The purpose is to improve the quality of the project activities. However, only 155 (36%)

respondents participated in the in-service training. By simple calculation, 274 (64%) respondents missed

essential knowledge on how the ASEI/PDSI should be monitored and evaluated in order to determine its

impacts on the learners.

Influence of Motivational Strategies on Respondents’ Participation in the SMASSE INSET

Respondents were asked to respond either as Very Low Priority (VLP); Low Priority (LP); Undecided (U);

High Priority (HP) or Very High Priorities (VHP) to the questionnaire items.

A score of 1 was given to VLP, 2 to LP, 3 to Undecided, 4 to HP and 5 to VHP. For each motivational

strategy, the mean score was computed for the cumulative responses. The mean was used to explain the level

of influence of the motivational strategy on the respondents’ attendance of the SMASSE in-service training.

Influence of motivational strategies on respondents’ attendance in the INSET

How has the provision of the following

motivational incentives influenced your

attendance of the SMASSE INSET:

V

L

P

L

P

U H

P

V

H

P

Mean

Ẍ

1. Provision of quality meals for participants

during in-service training.

2

(0.5%)

6

(1.4%)

9

(2.1%)

21

(5.0%)

390

(91%) 4.84

2. Adequate supply of training materials

during in-service training.

3

(0.7%)

9

(2.1%)

11

(2.5%)

66

(15.5%)

339

(79%) 4.69

3. Enabling easy accessibility to SMASSE

resources after in-service training for the

purpose of teaching & learning in your school.

9

(2.1%)

26

(6.0%)

26

(6.0%)

88

(20.4%)

279

(65.0%) 4.40

4. Provision of adequate allowances for

participants.

1

(0.2%)

1

(0.2%)

1

(0.2%)

3

(0.7%)

421

(98.2%) 4.95


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5. Provision of entertainment and recreational

facilities

112

(26%)

124

(29%)

56

(13%)

43

(10%)

94

(22%) 2.73

6. Provision of adequate security during the

in-service.

1

(0.2%)

3

(0.6%)

3

(0.6%)

32

(7.4%)

390

(91%) 4.88

7. Consideration of SMASSE INSET

certificates by TSC as basis of promotion of

participants at end of 4th-cycle.

2

(0.5%)

7

(1.6%)

2

(0.5%)

10

(2.3%)

406

(94.7%) 4.90

8. Accessibility to SMASSE in-service

training centre

1

(0.2%)

3

(0.7%)

1

(0.2%)

13

(3.0%)

409

(95.4%) 4.91

Results from table 4.4 show how respondents prioritize different motivational strategies. The mean for

cumulative response for every motivational strategy was calculated from the following relation:

Mean = Sum of product of responses and priority score

Number of respondents

The computed mean was used to explain how the strategy influenced participation in the in-service training.

Respondents were asked to prioritize provision of quality meals during in-service training. A mean of 4.84

was obtained implying that provision of quality meal to respondents highly influence their participation in the

in-service training. Similarly, respondents highly prioritize provision of adequate training resources during the

INSET. A mean of 4.69 was calculated showing that adequate resources during SMASSE in-service training

highly influence respondents’ participation.

From table 4.4, 367 (86%) respondents indicate that accessing SMASSE resources for the purpose of teaching

and learning in their school highly influences their attendance. The response on provision of allowance to

participant indicated a very high priority. A mean of 4.95 was obtained implying that provision of adequate

allowance to participants highly influence their participation.

Respondents were asked to prioritize how provision of entertainment and other recreational facilities during

in-service training influence participation. The findings were represented in table 4.4 from which a mean of

2.73 was computed. This implies that provision of entertainment does not influence participation.

Provision of adequate security during in-service training was considered as a very high priority by

respondents. A mean for cumulative responses on provision of security was presented in table 4.4. It implies

that good security at the INSET centre will motivate participation by respondents.

Respondents were asked to prioritize how consideration of SMASSE INSET certificates by Teachers Service

Commission (TSC) for promotion. The findings were presented in table 4.4 from which a mean of 4.90 was

obtained indicating a very high priority. This implies that consideration of certificate by TSC will highly

influence participation.


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Finally, the researcher asked respondents to prioritize how accessibility to in-service training centres

influenced participation. A mean of 4.91 was calculated from the findings presented in table 4.4 under

accessibility to INSET centres implying a very high priority. From the mean, accessibility to training centres

highly influence respondents’ participation in the INSET.

Therefore, from the aforementioned analysis, the findings show that provision of motivation to participant

influence participation. The mean of motivational strategies were ranked as shown.

Ranking of Prioritized Motivational Strategies

The responses on provision of adequate allowance and accessibility to INSET centres highly influence their

participation. Respondents will not attend in-service training if certificate for participation have no value

attached; security at the INSET centre is not adequate and; meals provided are not of good quality. However,

provision of entertainment and other recreational facilities will not influence participation. Respondents will

attend with or without entertainment.

From table 4.5, the study concludes about objective one that provision of adequate allowance; accessibility to

in-service training centres; consideration of certificates for promotion; provision of adequate security and

quality meals highly motivate teachers to participate in the in-service training. Availability of sufficient

training materials during training and accessing SMASSE resources by mathematics and science teachers to

use in their schools for teaching and learning motivates respondents’ participation. Provision of entertainment

and recreational facilities do not motivate respondents to participate in the in-service training.

A study by Ndirangu, (2013) on factors influencing teachers’ level of implementation of strengthening of

mathematics and science in secondary education, did not consider provision of adequate allowance to

participants and accessibility to INSET centres as motivational strategies influencing participation. This study

therefore sought to fill the gap.

Motivational Strategy Mean

1. Provision of adequate allowances to participants. 4.95

2. Accessibility to SMASSE in-service training centre. 4.91

3. Consideration of SMASSE certificates for promotion. 4.90

4. Provision of adequate security at INSET centres. 4.88

5. Provision of quality meals during in-service training. 4.84

6. Adequate training materials during in-service training. 4.69

7. Accessing SMASSE resources for teaching/learning. 4.40

8. Provision of entertainment and other recreational facilities. 1.73


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CONCLUSION

The study concludes that respondents will attend in-service training if adequate allowances are given and the

INSET centres are easily accessible. If certificates of participation are considered for promotion, then

respondents will participate in the in-service training. Provision of adequate security, quality meals and

adequate training materials during in-service training highly motivates respondents to participate in the

SMASSE INSET programme.

It was concluded that motivation is a driving force behind respondents’ participation in the SMASSE in-

service training. If respondents are not motivated, they will not participate in the in-service training implying

that implementation of SMASSE tenets are at risk. This is reflected in poor performance by students at

national examinations.

RECOMMENDATION

Based on the findings and conclusions above, it was recommended that the national SMASSE office and the

MoEST should consider the views of teachers on motivation when planning for INSETs to enhance effective

implementation of ASEI-PDSI innovation.

REFERENCES

Ajzen, I. (2002): Perceived behavioral control, self-efficacy, locus of control, and

the theory of planned behavior. Journal of Applied Social Psychology, 32,

665-683.

CEMASTEA, (2008): Handbook on Management of district SMASSE Programmes.

Nairobi:SMASSE-JICA.

Dylan, D. (2007): A guide to successful innovation in 21st century: Chicago Illinois,

The University of Chicago.

JICA, (2000): Japanese International Cooperation Agency:Tokyo; JICA, Unpublished.

KNEC, (2014): Kenya National Examinational Council, Newsletter, KNEC: 2014.

Koul, L (1993): Estimations of parameters of Autoregrssion, [ArCp] models.

MoEST, (1998): Strengthening Mathematics and Science in Secondary Education

Baseline Studies. Unpublished

Mugenda M. O and Mugenda G.A (2003) Research Methods, Quantitative and

Qualitative Approaches, Nairobi ACTS Press.


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Ndirangu, C. W. (2006). An evaluation of smasse in-service project in Biology in

Kajiado district, Kenya. Master in Education Project: VDM.

Orodho, O. J. (2009): Research Instruments: Validity of Research instruments and data Reliability.

Orodho, O. J. (2009): Research Instruments: Validity of Research instruments and data Reliability.

Rogers E.M (1995): Diffusion of Innovation, (4th

ed). New York: The Free Press.

SMASSE, (2008): Handbook on management of district SMASSE Programmes. Nairobi

JICA.Unpublished.

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